We produce podcast transcripts by parsing podcast audio through a danish transcription system developed at the Technical University of Denmark (DTU) as part of the Danspeech project.https://danspeech.github.io/danspeech/html/index.html The system is based on the wav2vec 2.0 framework [1]}. The model was pre-trained using approximately 945 hours of podcast episodes and 400 hours of audiobooks, and fine-tuned using the Connectionist Temporal Classification [2]} (CTC) loss function on 200 hours of labeled data from the Nordisk Språkteknologi (NST) danish training datasethttps://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-55/ and 267 hours of aligned audiobook data. The Fairseq library [3]} was used for both the pre-training procedure as well as fine-tuning.
During inference, the ASR engine performs prefix beam search [4]} with an open-source danish 3-gram language modelhttps://danspeech.github.io/danspeech/html/lms.html#danspeech.language_models.DSLWikiLeipzig3gram when decoding the probabilities emitted from the wav2vec 2.0 model.
